package xla
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service XlaService
xla_service.proto:49/////////////////////// Global data requests
rpc Compile (, )
xla_service.proto:132Compiles the provided computation into executable. Returns the handle of the executable.
message
xla.proto:348optional computation = 1
The graph to be compiled.
optional execution_options = 2
Options that affect how XLA compiles code to service this request.
repeated input_shape_with_layout = 3
The layouts of the input arguments. If not set, the default layout will be used. Although the real arguments are not needed in compilation, the layouts of the arguments can affect the compilation.
message
xla.proto:361optional handle = 1
The handle to the executable.
Computes the value of a constant expression. The request contains the computation graph for the constant expression.
message
xla.proto:406optional computation = 1
optional output_layout = 2
message
xla.proto:411optional literal = 1
A LiteralProto is returned directly for this request.
Creates a channel handle that can be used to transfer data between two computations via a pair of Send and Recv instructions.
message
xla.proto:333ChannelHandle.ChannelType channel_type = 1
message
xla.proto:337optional channel = 1
rpc DeconstructTuple (, )
xla_service.proto:62Deconstructs a tuple. Returns a newly created GlobalDataHandle for each element in the tuple.
message
xla.proto:416optional tuple_handle = 2
message
xla.proto:420repeated element_handles = 1
rpc Execute (, )
xla_service.proto:137Invokes the provided executable with the provided global data passed as immutable arguments. The request contains the handle to the executable. Returns global data output and execution timing.
message
xla.proto:366optional handle = 1
repeated arguments = 2
The shape and layout of the arguments must be the same as the those of the executable's parameters.
Invokes the provided list of computations in parallel with the provided global data for each computation. Returns a list of global data output and execution timing.
message
xla.proto:384repeated requests = 1
message
xla.proto:393repeated responses = 1
Requests the statistics of the given computation.
message
xla.proto:324optional computation = 1
optional debug_options = 2
message
xla.proto:329optional stats = 1
Requests one or more device handles from the target. The returned device handles can be used to specify the device on which to execute computations or transfer data.
message
xla.proto:266int64 device_count = 1
message
xla.proto:270repeated device_handles = 1
rpc GetShape (, )
xla_service.proto:74Requests the shape of the referenced global data.
message
xla.proto:454optional data = 1
message
xla.proto:458optional shape = 1
rpc LoadData (, )
xla_service.proto:83Loads a variable number of values with a given element type from ColumnIO.
message
xla.proto:424string columnio_tablet_path = 1
Describes the path of the ColumnIO tablet to load.
string columnio_field = 2
Describes the field to load within the ColumnIO tablet.
optional element_shape = 3
Individual element shape, excluding rows.
int64 offset = 4
Warning: ColumnIO does not support random-access, so use offset with caution in performance-critical scenarios.
int64 limit = 5
Maximum number of elements (with shape element_shape) to load.
bool zip = 6
If more than one item is requested (via limit > 1), then this request attribute zips together the produced vectors.
message
xla.proto:446optional data = 1
optional data_shape = 2
int64 available_rows = 3
int64 rows_loaded = 4
int64 nanoseconds = 5
rpc ResetDevice (, )
xla_service.proto:108Resets the device, clearing all existing state on the device.
message
xla.proto:317optional device_handle = 1
message
xla.proto:321(message has no fields)
Transferred literal from the Outfeed buffer of the device.
message
xla.proto:304optional shape_with_layout = 1
This optional field directs the service to return the literal in this layout. A shape is used to hold the layout to accommodate tuples.
int64 replica_id = 2
optional device_handle = 3
message
xla.proto:313optional literal = 1
rpc TransferToClient (, )
xla_service.proto:87Transfers the given global data to the client in the form of a Literal.
message
xla.proto:274optional data = 1
optional shape_with_layout = 2
This optional field directs the service to return the literal in this layout. A shape is used to hold the layout to accommodate tuples.
message
xla.proto:282optional literal = 1
rpc TransferToInfeed (, )
xla_service.proto:98Transfers the given literal to the Infeed buffer of the device.
message
xla.proto:295optional literal = 1
int64 replica_id = 2
optional device_handle = 3
message
xla.proto:301(message has no fields)
rpc TransferToServer (, )
xla_service.proto:93Transfers the given literal to the server to be stored in a global allocation, which is returned.
message
xla.proto:286optional literal = 1
optional device_handle = 2
message
xla.proto:291optional data = 1
rpc Unpack (, )
xla_service.proto:70Unpack requests that a global data handle, with a tuple shape, has global data handles created for each of its constituent members. This is the equivalent of the "destructuring assignment" present in various programming languages.
message
xla.proto:462optional data = 1
message
xla.proto:466repeated tied_data = 1
rpc Unregister (, )
xla_service.proto:57Unregisters a global allocation. If the handle given is not currently allocated, a NOT_FOUND status is returned.
message
xla.proto:341repeated data = 1
message
xla.proto:345(message has no fields)
Waits until the given execution (aysnchronously launched) is complete, and returns the global data output.
message
xla.proto:397optional execution = 1
message
xla.proto:401optional output = 1
optional profile = 2
message
hlo.proto:337Serialization of BufferAllocation.
Used in:
int64 index = 1
int64 size = 2
bool is_thread_local = 3
bool is_tuple = 11
bool is_entry_computation_parameter = 5
bool is_constant = 12
int64 parameter_number = 6
repeated int64 parameter_shape_index = 10
bool maybe_live_out = 7
int64 color = 8
repeated assigned = 9
message
hlo.proto:340Assigned represents a single LogicalBuffer that is assigned to this BufferAllocation.
Used in:
int64 logical_buffer_id = 1
int64 offset = 2
int64 size = 3
message
hlo.proto:402Serialization of BufferAssignment.
Used in:
repeated logical_buffers = 1
repeated buffer_aliases = 2
repeated buffer_allocations = 3
repeated heap_simulator_traces = 4
message
hlo.proto:405Alias represents a source LogicalBuffer, and the buffer location that aliases it.
Used in:
int64 source_buffer_id = 1
optional location = 2
message
xla_data.proto:308Handle given to a user to represent a channel between two computations via a Send and Recv instruction pair. Channels are unbuffered, so Send Send instructions will be blocked until the data is transferred.
Used in:
int64 handle = 1
ChannelHandle.ChannelType type = 2
Used in: ,
CHANNEL_TYPE_INVALID = 0
Invalid primitive type to serve as default.
DEVICE_TO_DEVICE = 1
A channel for sending data between devices.
DEVICE_TO_HOST = 2
A channel for sending data from the device to the host. Can only be used with a Send operation.
HOST_TO_DEVICE = 3
A channel for sending data from the host to the device. Can only be used with a Recv operation.
message
xla_data.proto:220Statistics of a computation.
Used in:
double flop_count = 1
The number of floating point operations in the computation.
double transcendental_count = 2
The number of transcendental operations (e.g., exp) in the computation.
message
xla_data.proto:467Used in:
int64 input_batch_dimension = 7
The number of the dimension that represents batch in the input.
int64 input_feature_dimension = 8
The number of the dimension that represents features in the input.
repeated int64 input_spatial_dimensions = 11
The dimension numbers for the spatial dimensions that the window moves through in the input.
int64 kernel_input_feature_dimension = 3
The number of the dimension that represents input features in the convolutional kernel (rhs).
int64 kernel_output_feature_dimension = 4
The number of the dimension that represents output features in the convolutional kernel (rhs).
repeated int64 kernel_spatial_dimensions = 6
The dimension numbers for the spatial dimensions that the window moves through in the kernel (rhs). window.strides(0) is the stride in the kernel_spatial_dimensions(0) dimension.
int64 output_batch_dimension = 9
The number of the dimension that represents batch in the output.
int64 output_feature_dimension = 10
The number of the dimension that represents features in the output.
repeated int64 output_spatial_dimensions = 12
The dimension numbers for the spatial dimensions that the window moves through in the output.
message
xla.proto:63Debugging options for XLA. These options may change at any time - there are no guarantees about backward or forward compatibility for these fields.
Used in: , ,
string xla_generate_hlo_graph = 1
HLO modules matching this regex will be dumped to a .dot file throughout various stages in compilation (file names are LOG(INFO)'d). Set to ".*" to dump *all* HLO modules.
bool xla_hlo_graph_addresses = 2
Show addresses of HLO ops in graph dump.
string xla_hlo_graph_path = 4
Path to dump HLO graphs to.
bool xla_hlo_dump_as_graphdef = 5
Dump HLO graphs as TensorFlow GraphDefs.
string xla_log_hlo_text = 6
HLO modules matching this regex will be dumped to LOG(INFO). Set to ".*" to dump *all* HLO modules.
string xla_generate_hlo_text_to = 7
Dump all HLO modules as text into the provided directory path.
string xla_dump_optimized_hlo_proto_to = 8
Dump Hlo after all hlo passes are executed as proto binary into this directory.
bool xla_hlo_profile = 9
Instrument the computation to collect per-HLO cycle counts.
string xla_dump_computations_to = 10
Dumps computations that XLA executes into the provided directory path.
string xla_dump_executions_to = 11
Dumps parameters and results of computations that XLA executes into the provided directory path.
repeated string xla_disable_hlo_passes = 30
List of HLO passes to disable. These names must exactly match the pass names as specified by the HloPassInterface::name() method.
bool xla_disable_all_hlo_passes = 104
Disables all HLO passes. Notes that some passes are necessary for correctness and the invariants that must be satisfied by "fully optimized" HLO are different for different devices and may change over time. The only "guarantee", such as it is, is that if you compile XLA and dump the optimized HLO for some graph, you should be able to run it again on the same device with the same build of XLA.
int32 xla_backend_optimization_level = 31
Numerical optimization level for the XLA compiler backend; the specific interpretation of this value is left to the backends.
bool xla_embed_ir_in_executable = 33
Embed the compiler IR as a string in the executable.
string xla_dump_ir_to = 34
Dump the compiler IR into this directory as individual files.
bool xla_eliminate_hlo_implicit_broadcast = 35
Eliminate implicit broadcasts when lowering user computations to HLO instructions; use explicit broadcast instead.
bool xla_cpu_multi_thread_eigen = 60
When generating calls to Eigen in the CPU backend, use multi-threaded Eigen mode.
string xla_gpu_cuda_data_dir = 61
Path to directory with cuda/ptx tools and libraries.
bool xla_gpu_ftz = 62
Enable flush-to-zero semantics in the GPU backend.
bool xla_gpu_disable_multi_streaming = 63
Disable multi-streaming in the GPU backend.
bool xla_llvm_enable_alias_scope_metadata = 70
If true, in LLVM-based backends, emit !alias.scope metadata in generated IR.
bool xla_llvm_enable_noalias_metadata = 71
If true, in LLVM-based backends, emit !noalias metadata in the generated IR.
bool xla_llvm_enable_invariant_load_metadata = 72
If true, in LLVM-based backends, emit !invariant.load metadata in the generated IR.
bool xla_llvm_disable_expensive_passes = 73
If true, a set of expensive LLVM optimization passes will not be run.
repeated hlo_reduce_precision_options = 80
Options for inserting reduce-precision operations for numerical experimentation. This is a repeated field, as we may want to have multiple passes with different parameters.
bool xla_test_all_output_layouts = 90
This is used by ClientLibraryTestBase::ComputeAndCompare*. If true, the computation will run n! times with all permunations of layouts for the output shape in rank n. For example, with a 3D shape, all permutations of the set {0, 1, 2} are tried.
bool xla_test_all_input_layouts = 91
This is used by ClientLibraryTestBase::ComputeAndCompare*. If true, the computation will run for all permunations of layouts of all input arguments. For example, with 2 input arguments in 2D and 4D shapes, the computation will run 2! * 4! times.
bool xla_hlo_graph_sharding_color = 92
Assign colors based on sharding information when generating the Graphviz HLO graph.
bool xla_hlo_tfgraph_device_scopes = 93
Prefix the name scopes of the TF graph exports with "devX" device assignments, if available.
bool xla_gpu_use_cudnn_batchnorm = 94
If true, the GPU backend is free to use cudnn for HLO batch normalization ops.
string xla_dump_unoptimized_hlo_proto_to = 95
Dump HLO before any hlo passes are executed as proto binary into this directory.
string xla_dump_per_pass_hlo_proto_to = 96
Dump HLO after each pass as an HloProto in binary file format into this directory.
bool xla_cpu_use_mkl_dnn = 97
Generate calls to MKL-DNN in the CPU backend.
int32 xla_gpu_max_kernel_unroll_factor = 98
Maximum kernel unroll factor for the GPU backend.
bool xla_cpu_enable_fast_math = 99
When true, "unsafe" mathematical optimizations are enabled. These transformations include but are not limited to: - Reducing the precision of operations (e.g. using an approximate sin function, or transforming x/y into x * (1/y)). - Assuming that operations never produce or consume NaN or +/- Inf. - Assuming that +0 and -0 are indistinguishable.
bool xla_gpu_enable_fast_min_max = 100
When true we lower the Minimum and Maximum hlos in the GPU backend such that Min(NotNaN, NaN) = Min(NaN, NotNaN) = NotNaN. In other words, if flag this is true we don't propagate NaNs through Min and Max.
bool xla_gpu_crash_on_verification_failures = 101
Crashes the program when any kind of verification fails, instead of just logging the failures. One example is cross checking of convolution results among different algorithms.
int32 xla_force_host_platform_device_count = 102
Force the host platform to pretend that there are these many host "devices". All these devices are backed by the same threadpool. Defaults to 1. Setting this to anything other than 1 can increase overhead from context switching but we let the user override this behavior to help run tests on the host that run models in parallel across multiple devices.
bool xla_gpu_disable_ptxas_optimizations = 103
If set to true XLA:GPU invokes `ptxas` with -O0 (default is -O3).
bool xla_hlo_dump_as_html = 105
Dump HLO graphs as an HTML (DOT -> SVG inlined in HTML)
bool xla_hlo_evaluator_use_fast_path = 106
Enable fast math with eigen in the HLO evaluator.
map<string, string> xla_backend_extra_options = 500
Extra options to pass to the compilation backend (e.g. LLVM); specific interpretation of these values is left to the backend.
message
xla_data.proto:331DeviceAssignmentProto is a serialized form of DeviceAssignment class, which represents the device ids assigned to a set of replicated computations. See xla::DeviceAssignment class comment for more details.
int32 replica_count = 1
int32 computation_count = 2
repeated computation_devices = 3
message
xla_data.proto:337Each logical computation runs on replica_count physical devices. ComputationDevice represents the device ids assinged to the replicas.
Used in:
repeated int32 replica_device_ids = 1
message
xla_data.proto:297Handle given to a user that represents a replicated virtual device. Each replicated device represents N physical devices for execution where N is the number of replicas.
Used in: , , , , ,
int64 handle = 1
int64 device_count = 2
The number of model-parallel virtual devices that communicate via XLA Send/Recv instructions.
message
xla_data.proto:512Used in:
repeated int64 lhs_contracting_dimensions = 1
The dimension numbers that represent the 'lhs' contracting dimensions.
repeated int64 rhs_contracting_dimensions = 2
The dimension numbers that represent the 'rhs' contracting dimensions.
repeated int64 lhs_batch_dimensions = 3
The dimension numbers that represent the 'lhs' batch dimensions.
repeated int64 rhs_batch_dimensions = 4
The dimension numbers that represent the 'rhs' batch dimensions.
message
hlo.proto:255Used in:
repeated entries = 1
message
hlo.proto:279A list of bindings which indicates that the `target_dim_num` in the subshape `target_param_index` of parameter `target_param_num` is a dynamic dimension and its real dynamic size is represented by `dynamic_param_index` in parameter `dynamic_param_num`. As an example, imagine we have a program: ENTRY main { a = f32[] parameter(0) b = f32[10] parameter(1) ROOT root = (f32[], f32[10]) tuple(%a, %b) } Let's say 'b' (param index 1) is a dynamic shape whose input has an upperbound of 10 and real size is determined at runtime.'a' represents the real size of b's first dimension. In this case, the fields are set in the following way: dynamic_param_num = 1 dynamic_param_index = {} target_param_num = 0 target_param_index = {} target_param_dim = 0
Used in:
int64 dynamic_param_num = 1
repeated int64 dynamic_param_index = 2
int64 target_param_num = 3
repeated int64 target_param_index = 4
int64 target_param_dim_num = 5
message
xla.proto:376TODO(b/118493728): Remove this and ExecuteGraphParallelRequest and replace the uses with calls to Compile and Execute.
Used in:
optional computation = 1
repeated arguments = 2
optional execution_options = 3
Options that affect how XLA compiles and runs code to service this request.
message
xla.proto:388Used as response type in: XlaService.Execute
Used as field type in:
optional output = 1
optional profile = 2
message
xla_data.proto:283Handle given to a user that represents an execution that the user launched asynchronously on the device.
Used in: , ,
int64 handle = 1
message
xla.proto:244These settings control how XLA compiles and/or runs code. Not all settings will have an effect on every platform. When adding new fields, keep in mind that boolean fields default to false.
Used in: ,
optional shape_with_output_layout = 2
This optional field's layout is used as a hint when storing the output of this computation. Subsequent transfers of this output array to the client may be faster when using this layout. We use a Shape here to accommodate computations that return a tuple.
uint64 seed = 3
Used to seed random-number generators used in this computation. If this is 0, we generate a seed ourselves. TODO(b/32083678): Changing the seed unnecessarily forces a recompilation.
optional debug_options = 4
repeated device_handles = 5
This optional field specifies a particular set of devices to run the computation on. The computation will be partitioned across these devices. If not provided, the default device will be chosen.
message
xla_data.proto:254Profile data from the execution of a computation.
Used in: ,
bool compilation_cache_hit = 1
Whether the executable was read from the compilation cache.
int64 compile_time_ms = 2
The time in milliseconds spent to compile the computation. This only set if the executable was not read from the compilation cache (compilation_cache_hit == false).
int64 compute_cycle_count = 3
The number of cycles spent for the computation. This does not include the time taken for the data transfers between the host and the device. This is a target-dependent field and only used for debugging purposes.
int64 compute_time_ns = 4
The time in nanoseconds spent for the computation, without data transfer.
int64 compute_and_transfer_time_ns = 5
The time in nanoseconds spent for the entire computation, including the result data transfer time. Current implementation does not spend any cycles for the input data transfer since the memory is initialized with the proper values before the execution.
int64 executable_size_in_bytes = 6
The size of the binary code in the executable.
Used in:
FFT = 0
Forward FFT; complex in, complex out.
IFFT = 1
Inverse FFT; complex in, complex out.
RFFT = 2
Forward real FFT; real in, fft_length / 2 + 1 complex out
IRFFT = 3
Inverse real FFT; fft_length / 2 + 1 complex in,
A format specifies the method used by a layout to store an array in memory.
Used in:
INVALID_FORMAT = 0
TODO(b/120869032): Rename this to FORMAT_NONE or something else which better corresponds to its meaning.
DENSE = 1
The default layout, with exactly one storage location per element.
SPARSE = 2
A sparsely encoded layout, providing only the index/value pairs of non-zero elements.
message
xla_data.proto:426Describes the dimension numbers for a gather operation. See https://www.tensorflow.org/performance/xla/operation_semantics#gather for more details.
Used in:
repeated int64 offset_dims = 1
"Window indices" is a term for a set of indices that index into the interior of a dynamic-slice from the input tensor, the starting indices for which were computed from output_gather_dims (see the operation semantic for how this is defined) and the start_indices tensor. The window indices for a specific output index Out is computed as: i = 0 for (k : [0, input_tensor_shape.rank)) window_indices[k] = if k in collapsed_slice_dims then 0 else Out[offset_dims[i++]]
repeated int64 collapsed_slice_dims = 2
repeated int64 start_index_map = 3
This is interpreted as a map from i to start_index_map[i]. It transforms the gather index looked up from the start_indices tensor into the starting index in the input space.
int64 index_vector_dim = 4
The dimension in the start_indices input that contains the starting indices.
message
xla_data.proto:290Handle given to a user that represents a globally accessible allocation. Contrast this against a ComputationDataHandle, which is not globally accessible, since it only exists within a specific computation.
Used in: , , , , , , , , , , , ,
int64 handle = 1
message
hlo.proto:360A trace of a HeapSimulator run.
Used in:
repeated events = 1
bool whole_module_simulation = 2
message
hlo.proto:363The trace includes a list of events, where each event describes one action performed by the heap simulator.
Used in:
Event.Kind kind = 1
int64 buffer_id = 2
The id of the LogicalBuffer that the event applies to.
string computation_name = 3
The HloInstruction that the simulation was processing that caused this event to occur, identified by its computation and instruction name. E.g. buffers defined by instruction A are allocated when processing A.
string instruction_name = 4
int64 share_with_canonical_id = 5
The id of the canonical LogicalBuffer that the buffer shares with. Only set for SHARE_WITH events.
Used in:
ALLOC = 0
A memory region was allocated for the buffer.
FREE = 1
A memory region was freed for the buffer.
SHARE_WITH = 2
A buffer was shared with another (canonical) buffer. This is similar to ALLOC, except that instead of allocating a new region of memory, the memory region of the canonical buffer is directly re-used. Multiple buffers may share with the same canonical buffer. The lifetime of the canonical buffer is extended to the union of all lifetimes.
message
hlo.proto:197Serialization of HloComputation.
Used in:
string name = 1
repeated instructions = 2
The array of instructions is always in a valid dependency order, where operands appear before their users.
optional program_shape = 4
int64 id = 5
The id of this computation.
int64 root_id = 6
The id of the root of the computation.
message
hlo.proto:229Used in:
repeated entries = 1
message
hlo.proto:243The following proto describes a pair of aliased an input (described by parameter number and a ShapeIndex of the parameter) and an output (described by a ShapeIndex of the root instruction). For example: entry = { output_shape_index={1}, parameter_number=0, parameter_shape_index={1, 2}, } This entry indicates that the first paremter's {1, 2} element is aliased with the {1} element of the root instruction.
Used in:
repeated int64 output_shape_index = 1
ShapeIndex of the root hlo.
int64 parameter_number = 2
Number of the parameter in entry computation.
repeated int64 parameter_shape_index = 3
ShapeIndex of the parameter instruction.
message
hlo.proto:38Serialization of HloInstruction. Next ID: 58
Used in:
string name = 1
string opcode = 2
optional shape = 3
optional metadata = 7
optional literal = 8
Literal, only present for kConstant.
int64 parameter_number = 9
Parameter number is only present for kParameter.
string fusion_kind = 11
Fusion state, only present for kFusion.
int64 tuple_index = 13
Index for kGetTupleElement.
repeated int64 dimensions = 14
Dimensions present for some operations that require reshaping or broadcasting, including Reshape, Reduce, ReduceWindow, and Reverse.
optional window = 15
Describes the window in a windowed operation such as convolution.
optional convolution_dimension_numbers = 16
Describes the dimension numbers used for a convolution.
int64 feature_group_count = 50
The number of feature groups. Used for a convolution. Must be a divisor of the input feature dimension and output feature dimension. If not specified, it will use a default value of 1.
repeated slice_dimensions = 17
int32 exponent_bits = 18
The bit sizes for a reduce-precision operation.
int32 mantissa_bits = 19
repeated int64 dynamic_slice_sizes = 20
Describes the [start, start + size) range size for a dynamic slice ('start' is specified dynamically in the second operand of the operation).
optional padding_config = 21
The padding configuration that describes the edge padding and interior padding of this pad instruction. Only set for pad instructions.
bytes outfeed_config = 22
Outfeed configuration information, only present for kOutfeed.
RandomDistribution distribution = 23
The distribution requested for random number generation. Only present for kRng.
float epsilon = 24
A small float number added to the variance to avoid divide-by-zero error. Only present for kBatchNormTraining.
int64 feature_index = 25
An integer value representing the index of the feature dimension. Only present for kBatchNormTraining.
int64 channel_id = 26
Represents a unique identifier for each Send/Recv instruction pair. Only present for kSend or kRecv.
bytes infeed_config = 27
The string representation of the infeed configuration.
string custom_call_target = 28
Name of a external target (eg, global symbol) to call, only present for kCustomCall.
string custom_call_opaque = 53
Opaque string, only present for kCustomCall.
optional outfeed_shape = 29
Shape of outfeed request.
optional dot_dimension_numbers = 30
Describes the dimension numbers used for a dot operation
FftType fft_type = 31
FFT type (FFT, IFFT, etc).
repeated int64 fft_length = 32
FFT length.
optional gather_dimension_numbers = 33
Gather dimension numbers.
repeated int64 gather_slice_sizes = 34
string channel_name = 41
Compute Host.
int64 cost_estimate_ns = 42
int64 id = 35
The id of this instruction.
repeated int64 operand_ids = 36
repeated int64 control_predecessor_ids = 37
repeated int64 called_computation_ids = 38
optional sharding = 40
string backend_config = 43
Backend configuration for the instruction. Has backend-specific meaning.
repeated replica_groups = 49
Cross replica op fields.
int64 all_reduce_id = 45
string cross_replica_sum_barrier = 46
bool is_host_transfer = 47
Whether this Send/Recv instruction transfers data to/from the host. Only present for Send and Recv instructions and their SendDone and RecvDone partners.
optional scatter_dimension_numbers = 48
optional precision_config = 51
Precision configuration for the instruction. Has backend-specific meaning.
repeated source_target_pairs = 52
Collective permute field.
optional domain_entry_sharding = 54
Sharding for kDomain instructions.
optional domain_exit_sharding = 55
bool constrain_layout = 56
For custom call this indicates that the layouts are constrained. If constrain_layout is true then the 'shape' field must contain a layout, and 'operand_shapes_with_layout' must contain a shape with layout for each operand.
repeated operand_shapes_with_layout = 57
message
hlo.proto:86Describes the [begin, end) index range and stride for slices.
Used in:
int64 start = 1
int64 limit = 2
int64 stride = 3
message
hlo.proto:396An abstraction representing a set of HLO module built to run concurrently across different devices.
string name = 1
repeated hlo_modules = 2
message
hlo.proto:291Serialization of HloModule.
Used in: , , , , ,
string name = 1
string entry_computation_name = 2
int64 entry_computation_id = 6
repeated computations = 3
The array of computations is always in a valid dependency order, where callees appear before their callers.
optional host_program_shape = 4
The host program shape (with layout) of the entry computation.
int64 id = 5
The id of this module.
optional schedule = 7
The schedule for this module.
optional input_output_alias = 8
Describes alias information between inputs and outputs.
optional dynamic_parameter_binding = 9
Describes how to pretty-print a profile counter array gathered for a specific HloModule.
repeated computation_infos = 1
HloComputationInfos for every HloComputation in the HloModule.
int64 profile_counters_size = 2
The size of the profile counters array we will pretty-print.
map<string, int64> extra_metrics = 3
Maps extra metric name to the index into the profile counters array.
string entry_computation = 4
Name of the entry computation.
Pretty-printer information about an HloComputation.
Used in:
string name = 1
int64 profile_index = 2
The index into the profile counters array for the HloComputation corresponding to this HloComputationInfo.
repeated instruction_infos = 3
HloInstructionInfos for every HloInstruction in the HloComputation for corresponding to this HloComputattionInfo.
Pretty-printer information about an HloInstruction.
Used in:
string long_name = 1
string short_name = 2
string category = 3
float flop_count = 4
Metrics computed by HloCostAnalysis.
float transcendental_count = 5
float bytes_accessed = 6
float optimal_seconds = 7
int64 profile_index = 8
The index into the profile counters array for the HloInstruction corresponding to this HloInstructionInfo.
message
hlo.proto:417Grouping message that contains all of the information above.
Used in: ,
optional hlo_module = 1
optional buffer_assignment = 3
message
xla.proto:24Options for the HLO insert-reduce-precision-operations pass.
Used in:
HloReducePrecisionOptions.Location location = 1
uint32 exponent_bits = 2
Exponent and mantissa bit counts for the reduced precision.
uint32 mantissa_bits = 3
repeated uint32 opcodes_to_suffix = 4
Operations matching these opcodes should be suffixed with reduce-precision operations.
repeated string opname_substrings_to_suffix = 5
Operations with names containing these substrings should be suffixed with reduce-precision operations.
Where and when the reduce-precision operations will be added.
Used in:
OP_INPUTS = 0
Add reduce-precision operations to the inputs of selected instructions. This is done before any optimization occurs.
OP_OUTPUTS = 1
Add reduce-precision operations to the outputs of selected instructions. This is done before any optimization occurs.
UNFUSED_OP_OUTPUTS = 2
After operation-fusion occurs, add reduce-precision operations to the outputs of any selected instructions that have not been fused into fusion instructions.
FUSION_INPUTS_BY_CONTENT = 3
After operation-fusion occurs, add reduce-precision operations to the outputs of any fusion instructions that contain operations matching the selection criteria.
FUSION_OUTPUTS_BY_CONTENT = 4
After operation-fusion occurs, add reduce-precision operations to the outputs of any fusion instructions that contain operations matching the selection criteria.
message
hlo.proto:220Serialization of an HLO schedule. An HLO schedule contains a total order of instructions for each non-fusion computation in the module.
Used in:
map<int64, > sequences = 1
Map from computation id to sequence.
message
hlo.proto:221Used in:
repeated int64 instruction_ids = 1
message
hlo.proto:428Encapsulates HloProto together with the arguments, result, and execution_platform. This message is used for purposes such as analysis/replay/file-storage.
Used in:
optional hlo = 1
The hlo graph.
repeated arguments = 2
The arguments passed to the graph.
optional result = 3
The result of the graph.
string execution_platform = 4
The name of the platform used to run the graph.
message
xla_data.proto:134A layout describes how the array is placed in (1D) memory space. This includes the minor-to-major ordering of dimensions within a shape. Clients must specify the layouts of input Literals to the computation. Layouts specified in interior operations which take Shapes (for example, Convert) are ignored. See the XLA documentation for more information on shapes and layouts. LINT.IfChange
Used in: ,
Format format = 4
The method used to store the data in memory. The format determines which of the other fields are used by the layout.
repeated int64 minor_to_major = 1
Sequence of dimension numbers, from minor (fastest varying index) to major (slowest varying index). This field is required.
int64 max_sparse_elements = 5
The maximum number of elements that can be stored for SPARSE formats. This can be used to determine the maximum size in bytes of arrays stored in memory. This field must be unset unless the format is SPARSE.
repeated tiles = 6
A sequence of tiles, starting from the tile that's applied first to the Shape. TODO(b/119839262): implement tiling in each backend or add Unimplemented error.
int64 element_size_in_bits = 7
Bit size of each element. If the size is bigger than what the element type requires, the value is stored in the least significant bits and the additional most significant bits are filled with 0's. TODO(b/119839262): implement in each backend or add Unimplemented error.
message
xla_data.proto:349Literals are used when the server and client need to exchange materialized data / results. Literals are also used to describe constants used in computations. Transfers to/from the client are encoded in literal form, and the structure of the repeated fields is implied by the shape.
Used in: , , , , , , ,
optional shape = 1
repeated bool preds = 2
bytes s8s = 15
bytes u8s = 3
repeated int32 s32s = 4
repeated int64 s64s = 5
repeated uint32 u32s = 6
repeated uint64 u64s = 7
repeated float f32s = 8
repeated double f64s = 9
repeated float c64s = 12
Stored as interleaved real, imag floats.
repeated tuple_literals = 10
bytes f16s = 11
The F16s, BF16s, U16s and S16s are encoded in little endian byte order
bytes bf16s = 13
bytes u16s = 16
bytes s16s = 17
repeated int64 sparse_indices = 14
Next = 18
message
hlo.proto:316Serialization of LogicalBuffer.
Used in:
int64 id = 1
int64 size = 2
optional defined_at = 3
The location where the buffer is defined.
int64 color = 4
message
hlo.proto:319Location represents an instruction and its shape index, which uniquely identifies a point where a buffer is needed.
Used in: ,
string computation_name = 1
NOTE: module_name isn't necessary, since all LogicalBuffers are associated with a single HloModule.
string instruction_name = 2
repeated int64 shape_index = 3
message
xla_data.proto:232Symbolization metadata for HLO Instructions. This metadata is used for debugging XLA code generation, as well as performance profiling of XLA-generated executables.
Used in:
string op_type = 1
The framework op name that generated this XLA op. Frameworks that build on top of XLA should mirror the names of their ops back to users by specifying the op_type. In this way, even if the framework's "ops" are implemented as multiple XLA HLO Ops, they can be grouped appropriately. (e.g. if a SoftMax layer is emitted into XLA as multiple ops, then each op should have the op_type be "SoftMax".)
string op_name = 2
The user-specified name of the op. This name is often unique within a computation. Note: some frameworks add auto-generated names if the user does not provide one.
string source_file = 3
Indicate a file and line that this op is associated to in a user's program. e.g. it could be the file and line of user code that generated the op.
int32 source_line = 4
message
xla_data.proto:537Used in:
OpSharding.Type type = 1
optional tile_shape = 2
The shape of the sharded tile.
repeated int64 tile_assignment_dimensions = 3
The shape of the tile assignment tensor - this must be the same rank as tile_shape and the product of its dimensions must equal tile_assignment_devices.size().
repeated int64 tile_assignment_devices = 4
Flattened list of device IDs. The order of flattening is the same as used by IndexUtil::MultiToLinearIndex(tile_assignment_shape).
repeated tuple_shardings = 5
If type == TUPLE, the sub-shardings, one per leaf node in the tuple shape, in pre-order. The tuple shape could be nested; here we store just a flattened list of all leaves in the tuple shape. Note that the tuple shape is not stored here; shardings do not store the shapes to which they are applied, this is inferred from the instruction this sharding gets attached to.
Used in:
REPLICATED = 0
This sharding is replicated across all devices (implies maximal, all other fields are unused).
MAXIMAL = 1
This sharding is maximal - one device runs the entire operation.
TUPLE = 2
This sharding is a tuple - only the tuple_shardings field is valid.
OTHER = 3
None of the above; tile_shape and tile_assignment are both used.
message
xla_data.proto:83Describes the padding configuration for Pad operation. The padding amount on both edges as well as between the elements are specified for each dimension.
Used in:
repeated dimensions = 1
The padding configuration for all dimensions.
message
xla_data.proto:85Describes the padding configuration for a dimension.
Used in:
int64 edge_padding_low = 1
Padding amount on the low-end (next to the index 0). May be negative.
int64 edge_padding_high = 2
Padding amount on the high-end (next to the highest index). May be negative.
int64 interior_padding = 3
Padding amount between the elements. May not be negative.
message
xla_data.proto:584Used to indicate the precision configuration. It has backend specific meaning.
Used in:
repeated PrecisionConfig.Precision operand_precision = 1
Used in:
DEFAULT = 0
HIGH = 1
HIGHEST = 2
Primitive types are the individual values that can be held in rectangular multidimensional arrays. A description of the rectangular multidimensional array dimensions / primitive type is given by Shape, below.
Used in:
PRIMITIVE_TYPE_INVALID = 0
Invalid primitive type to serve as default.
PRED = 1
Predicates are two-state booleans.
S8 = 2
Signed integral values of fixed width.
S16 = 3
S32 = 4
S64 = 5
U8 = 6
Unsigned integral values of fixed width.
U16 = 7
U32 = 8
U64 = 9
F16 = 10
Floating-point values of fixed width. Note: if f16s are not natively supported on the device, they will be converted to f16 from f32 at arbirary points in the computation.
F32 = 11
BF16 = 16
Truncated 16 bit floating-point format. This is similar to IEEE's 16 bit floating-point format, but uses 1 bit for the sign, 8 bits for the exponent and 7 bits for the mantissa.
F64 = 12
C64 = 15
Complex values of fixed width.
Paired F32 (real, imag), as in std::complex<float>.
TUPLE = 13
A tuple is a polymorphic sequence; e.g. a shape that holds different sub-shapes. They are used for things like returning multiple values from a computation; e.g. a computation that returns weights and biases may have a signature that results in a tuple like (f32[784x2000], f32[2000]) If a shape proto has the tuple element type, it may not have any entries in the dimensions field.
OPAQUE = 14
An opaque type used for passing context-specific data to a custom operation. Shapes of this primitive type will have empty dimensions and tuple_shapes fields.
TOKEN = 17
A token type threaded between side-effecting operations. Shapes of this primitive type will have empty dimensions and tuple_shapes fields.
message
xla_data.proto:213Shape of the parameters and output of a computation (like a traditional function signature).
Used in: , ,
repeated parameters = 1
optional result = 2
repeated string parameter_names = 3
Used in:
RNG_INVALID = 0
RNG_UNIFORM = 1
Creates a uniform-distribution-generated random number on the semi-open interval [parameter[0], parameter[1]).
RNG_NORMAL = 2
Creates a normal-distribution-generated random number with mean parameter[0] and standard deviation parameter[1].
message
xla_data.proto:570Describes the replica groups in a cross replica op (e.g., all-reduce and all-to-all).
Used in:
repeated int64 replica_ids = 1
The ids of the replicas that belongs to the same group. The ordering of the ids matters in some op (e.g., all-to-all).
message
xla_data.proto:457Describes the dimension numbers for a scatter operation. All the fields are similar to the corresponding fields in GatherDimensionNumbers. Differences are noted below.
Used in:
repeated int64 update_window_dims = 1
The set of dimensions in the updates shape that are window dimensions.
repeated int64 inserted_window_dims = 2
The set of window dimensions that must be inserted into the updates shape.
repeated int64 scatter_dims_to_operand_dims = 3
int64 index_vector_dim = 4
message
xla_data.proto:184A shape describes the number of dimensions in the array, the size of each dimension, and the primitive component type. Tuples are a special case in that they have rank zero and have tuple_shapes defined. See the XLA documentation for more information on shapes and layouts. LINT.IfChange
Used in: , , , , , , , , , ,
PrimitiveType element_type = 2
The element type for this shape.
repeated int64 dimensions = 3
The size (number of elements) for each dimension. In XLA, dimensions are numbered from 0 to N-1 for an N-dimensional array. The first element of 'dimensions' is the size of dimension 0, the second element is the size of dimension 1, and so forth. Empty list indicates a scalar.
repeated tuple_shapes = 4
For tuples only, the shapes of constitutent shapes in the tuple sequence.
optional layout = 5
The layout used to back this shape.
message
xla_data.proto:577Describes the source target pair in the collective permute op.
Used in:
int64 source = 1
int64 target = 2
message
xla_data.proto:116Describes a tile used in tiling-based layout. Refer to g3doc/third_party/tensorflow/compiler/xla/g3doc/layout_with_tiling.md for details about tiling-based layout.
Used in:
repeated int64 dimensions = 1
Number of elements in each dimension of the tile. It's ordered from the most major dimension of the tile to the most minor dimension of the tile. The dimensions correspond to a suffix of the dimensions of the shape being tiled.
message
xla_data.proto:418Describes the windowing in an operation such as convolution. The window is moved across a base area and for each position of the window a computation is performed. The field below describes the window and the movement of the window across a base area.
Used in:
repeated dimensions = 1
message
xla_data.proto:371Used in:
int64 size = 1
The size of the window in this dimension. For a rectangle, this would be the width or height.
int64 stride = 2
The stride at which the window moves across the base area in this dimension. In other words, this is the spacing between different positions of the window in this dimension.
int64 padding_low = 3
If positive, means the amount of padding to add to the base area at the low end of this dimension; if negative, its negative means the number of elements removed from the low end of this dimension. For example, in the horizontal dimension of a rectangle, this would be the number of padding values to pad on the left, given that indices increase when going right. The actual padding value depends upon the context. Convolution pads with zeros. ReduceWindow and SelectAndScatter pads with the reduce function's init value.
int64 padding_high = 4
As padding_low, but on the high end of this dimension. For example, in the horizontal dimension of a rectangle, this would be the number of values to pad on the right, given that indices increase when going right.
int64 window_dilation = 5
Dilation factor of the sliding window in this dimension. A dilation factor of 1 means no dilation. window_dilation - 1 no-op entries ("holes") are implicitly placed between each kernel element. This value may not be less than 1. See documentation for convolution.
int64 base_dilation = 6
Dilation factor of the base area in this dimension. A dilation factor of 1 means no dilation. base_dilation - 1 no-op entries ("holes") are implicitly placed between each base area element. This value may not be less than 1. See documentation for convolution.
bool window_reversal = 7
Window reversal means that this dimension was logically reversed before the operation.